Industrial internet supply chain asynchronous collaborative privacy computing method based on multi-party security computing

Abstract

The embodiment of the invention provides an industrial internet supply chain asynchronous collaborative privacy calculation method based on multi-party security calculation, which comprises the following steps: designing a data acquisition and storage strategy, and providing basic data support for the whole asynchronous collaborative privacy calculation; an asynchronous computing task scheduling algorithm is determined, and computing tasks are reasonably distributed according to available computing resources and data acquisition time laws of enterprises; an event-driven calculation mode is designed, the situation that enterprise data collection is completed or calculation conditions are met is responded in time, and a corresponding calculation task is triggered; a safe data interaction and cooperative processing algorithm is designed, encryption data of different enterprises are effectively integrated and cooperatively calculated, and an accurate basis is provided for overall decision making of a supply chain. Through the scheme of the embodiment of the invention, the problem of how to allow a participant to carry out calculation and data interaction at different time nodes due to possible difference between computing resources and data availability of different enterprises in an industrial supply chain can be solved, and the flexibility and adaptability of a system are improved.

Description

Technical Field

[0001] The present application relates to the field of data privacy computing technology, and specifically to an asynchronous collaborative privacy computing method for an industrial Internet supply chain based on multi-party secure computing. Background Art

[0002] The asynchronous collaborative privacy computing method for the industrial Internet supply chain based on multi-party secure computing is a technical solution that allows multiple companies to complete computing tasks together without leaking their sensitive information through encryption technology and protocol design. This method protects the data privacy of each participant while realizing data collaboration and sharing in multiple links of the supply chain. However, in practical applications, this synchronous computing method may encounter challenges due to significant differences in computing resources and data availability among different companies. To this end, researchers are committed to developing asynchronous collaborative computing mechanisms, which allow participants to flexibly perform calculations and data interactions at different time nodes according to their own circumstances, thereby improving the flexibility and adaptability of the system and better meeting actual business needs. Summary of the invention

[0003] In view of this, the embodiments of the present disclosure provide an asynchronous collaborative privacy computing method for the industrial Internet supply chain based on multi-party secure computing, which at least partially solves the problems existing in the prior art.

[0004] An asynchronous collaborative privacy computing method for industrial Internet supply chain based on multi-party secure computing, comprising:

[0005] Design data collection and storage strategies to provide basic data support for the entire asynchronous collaborative privacy computing. Personalized data collection plans ensure that enterprise data can be accurately obtained at different time nodes, and encrypted storage and distributed storage ensure data security and accessibility.

[0006] Determine the asynchronous computing task scheduling algorithm, reasonably allocate computing tasks according to the enterprise's available computing resources and data collection time patterns, make full use of the computing resources of different enterprises at different times, and improve computing efficiency.

[0007] Design an event-driven computing model to promptly respond to situations where enterprise data collection is completed or computing conditions are met, trigger corresponding computing tasks, and improve the system's response speed and flexibility.

[0008] Design secure data interaction and collaborative processing algorithms to effectively integrate and collaboratively calculate encrypted data from different companies, providing an accurate basis for overall supply chain decision-making, while relying on the data and computing resources provided by the first three steps.

[0009] Preferably, the data collection and storage strategy includes:

[0010] Develop personalized data collection plans for different enterprises, and collect data according to the operation time of the enterprise's production equipment. For example, an enterprise that produces at night will automatically collect data during the operation of the equipment at night;

[0011] The collected data is encrypted locally and distributed storage technology is used to store the encrypted data in distributed storage nodes locally or in the cloud to ensure data security and accessibility.

[0012] Preferably, the asynchronous computing task scheduling algorithm includes:

[0013] Assume that there are n enterprises participating in the asynchronous computing of the industrial Internet supply chain, and each enterprise i has its specific available computing resources Ri and data collection time rule Ti. Define the task set T = {T1, T2, ..., Tn}, and the resource set R = {R1, R2, ..., Rn};

[0014] Initialization: Create a task queue Q, which is initially empty. For each enterprise i, predict the next possible data collection completion time ti_next based on its data collection time rule Ti;

[0015] Task allocation step: Regularly check the current time t_current of each enterprise. For enterprise i, if t_current>=ti_next, add the computing task of the enterprise to the task queue Q. Traverse the task queue Q, and for each task Tj∈Q, find the enterprise k with sufficient available computing resources, so that Rk>=required resources (Tj). Assign task Tj to enterprise k, and update the available computing resources Rk=Rk-required resources (Tj) of enterprise k. Update the next data collection completion time ti_next of enterprise i=current time (t_current)+data collection cycle (Ti).

[0016] Preferably, the event-driven computing mode includes:

[0017] The event-driven computing model is adopted. When the data collection of a certain enterprise is completed or the computing conditions are met, the corresponding computing tasks are triggered. For example, when a night production enterprise completes a night production cycle and the data collection is completed, the system automatically triggers the data preprocessing and preliminary analysis computing tasks of the enterprise.

[0018] Preferably, the secure data interaction and collaborative processing algorithm includes:

[0019] Establish a secure data interaction protocol based on multi-party secure computing to ensure that encrypted data between enterprises operating at different time points can be effectively integrated. Through standardized encrypted data formats and interfaces, data can be smoothly circulated and correctly identified and processed between different enterprises.

[0020] Assume that there are m companies whose encrypted data need to be processed collaboratively, and the data set is D = {D1, D2, ..., Dm}. Establish a data status list L, and the initial status is that the data status of each company is "not ready". When the encrypted data Di of enterprise i is ready, update its status to "ready". Check the data status list L regularly, and start collaborative computing when the data status of all companies is "ready". Use multi-party secure computing technology to jointly calculate the encrypted data, such as calculating statistics such as average and sum. After the calculation is completed, the result is decrypted and returned to each company.

[0021] The disclosed embodiment provides an asynchronous collaborative privacy computing method for industrial Internet supply chain based on multi-party secure computing, including: designing data collection and storage strategies to provide basic data support for the entire asynchronous collaborative privacy computing, wherein the personalized data collection plan ensures that enterprise data can be accurately obtained at different time nodes, and encrypted storage and distributed storage ensure the security and accessibility of data. Determine the asynchronous computing task scheduling algorithm, reasonably allocate computing tasks according to the available computing resources and data collection time rules of the enterprise, make full use of the computing resources of different enterprises at different times, and improve computing efficiency. Design an event-driven computing mode to respond to the completion of enterprise data collection or the satisfaction of computing conditions in a timely manner, trigger corresponding computing tasks, and improve the response speed and flexibility of the system. Design a secure data interaction and collaborative processing algorithm to effectively integrate and collaboratively calculate the encrypted data of different enterprises, provide accurate basis for the overall decision-making of the supply chain, and rely on the data and computing resources provided by the first three steps. Through the solution of the disclosed embodiment, it is possible to solve the problem that there may be differences in computing resources and data availability of different enterprises in the industrial supply chain, how to allow participants to perform computing and data interaction at different time nodes, and improve the flexibility and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the exemplary implementation methods of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the embodiments of the present disclosure and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a flow chart of the asynchronous collaborative privacy computing method for the industrial Internet supply chain based on multi-party secure computing;

[0024] Figure 2 It is a flow chart of the data collection and storage strategy. DETAILED DESCRIPTION

[0025] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0026] Next, refer to Figure 1 , describes the asynchronous collaborative privacy computing method of the industrial Internet supply chain based on multi-party secure computing of the present invention. The method mainly includes four core steps: designing data collection and storage strategies, determining asynchronous computing task scheduling algorithms, designing event-driven computing modes, and designing secure data interaction and collaborative processing algorithms.

[0027] S101: Design data collection and storage strategies to provide basic data support for the entire asynchronous collaborative privacy computing. In this step, enterprises formulate personalized data collection plans based on their own business needs to ensure that the required data can be accurately obtained at different time points. For example, a manufacturing company may collect real-time data from the production line every few hours during peak production, and reduce the collection frequency during the off-season. Encrypted storage and distributed storage technologies are used to protect the security and accessibility of this data, ensuring that only authorized parties can access the data at the appropriate time and place. For example, a company's sensitive data is sharded and encrypted and stored on multiple servers. Each server only has part of the data fragments, and no party can reconstruct the complete data alone.

[0028] S102: Determine the asynchronous computing task scheduling algorithm, reasonably allocate computing tasks according to the available computing resources and data collection time rules of the enterprise, make full use of the computing resources of different enterprises at different times, and improve computing efficiency. The specific scheduling algorithm can be adjusted according to the actual situation of the enterprise, such as giving priority to allocating computing resources to enterprises that have completed data collection and have computing conditions. In one embodiment, assume that a supply chain contains three enterprises of different sizes: A, B and C. Enterprise A completed data collection at 10 am, while enterprises B and C completed data collection at 3 pm and 7 pm respectively. Through the computing task scheduling algorithm, the system immediately allocates computing tasks after the data collection of each enterprise is completed, making full use of the idle computing resources of the enterprise and avoiding idle computing resources.

[0029] S103: Design an event-driven computing model to promptly respond to the completion of enterprise data collection or the satisfaction of computing conditions, trigger corresponding computing tasks, and improve the response speed and flexibility of the system. For example, when enterprise A's data collection is completed, the system will automatically detect this event and immediately start the computing task to process enterprise A's data. At the same time, if data from other enterprises is required during the calculation process, the system will also detect and wait for the completion of these data in real time. For example, in a supply chain scenario, after enterprise A's data collection is completed, if some computing tasks require the data of enterprises B and C, the system will wait for the data collection of enterprises B and C to be completed before triggering further computing tasks to ensure the consistency and integrity of the computing process.

[0030] S104: Design secure data interaction and collaborative processing algorithms to effectively integrate and collaboratively calculate the encrypted data of different companies, and provide accurate basis for the overall decision-making of the supply chain. This step relies on the data and computing resources provided by the previous three steps. Specifically, the system uses multi-party secure computing technology to ensure that no participant can see the original data of other companies during the entire collaborative computing process, thereby ensuring the privacy of the data. For example, companies A, B, and C need to jointly calculate a supply chain optimization model, and the data of all parties are encrypted before joint calculation. Through technologies such as homomorphic encryption, the system can complete the required calculations without exposing the original data, and finally output the results for reference and use by each company.

[0031] Take the automobile manufacturing supply chain as an example, including automobile parts suppliers A, B, C and automobile assembly plant D. Supplier A mainly produces and collects data at night, and its computing resources are relatively idle during the day. The asynchronous computing task scheduling algorithm assigns some tasks that do not require high timeliness of data processing to supplier A during the day (such as: statistical analysis tasks for the production data of the past week). When supplier A completes a nighttime production cycle and completes data collection, the event-driven computing mode automatically triggers A's data preprocessing and preliminary analysis computing tasks (for example, calculating the key indicators such as the qualified rate and output of the parts produced this time). Suppliers B and C produce and collect data during the day, and their computing resources may be partially idle at night. The scheduling algorithm assigns some data preprocessing tasks to B and C at night. In terms of event drive, supplier B completed the delivery of a batch of key parts at a certain time during the day, triggering the data update and inventory adjustment computing tasks related to the delivery. The production process of automobile assembly plant D is relatively continuous, and the availability of its computing resources is relatively stable. The scheduling algorithm reasonably allocates some tasks closely related to the assembly progress (such as the analysis of real-time quality inspection data) according to D's production plan and resource situation. When it detects a fault in a production line, the event-driven mode immediately triggers the calculation tasks of fault diagnosis and maintenance plan generation. When the data of all participants are ready, the collaborative processing algorithm starts, comprehensively considering the parts production data provided by suppliers A, B, and C and the assembly progress and sales data of automobile assembly plant D, and through multi-party secure computing, these encrypted data are jointly calculated to obtain the predicted value of market demand.

[0032] In this supply chain, the asynchronous computing task scheduling algorithm ensures that the computing resources of each participant are reasonably utilized, and the event-driven computing model improves the response speed and flexibility of the system. The collaborative processing algorithm effectively integrates the data of all parties, providing a more accurate basis for the overall decision-making of the supply chain.

[0033] Suppose the collaborative processing algorithm predicts that the market demand for a certain car model will increase in the next month. Based on this result, the asynchronous computing task scheduling algorithm can adjust the task allocation and allocate more resources to suppliers A, B, and C to increase the production speed of parts. At the same time, the event-driven computing model can pay closer attention to quality indicators and delivery time during the supplier's production process to ensure timely response to changes in market demand. In this way, by comprehensively considering the results of the three algorithms, efficient collaborative operation of the supply chain can be achieved and overall competitiveness can be improved.

[0034] In summary, this method solves the problem of differences in computing resources and data availability among different enterprises in the industrial supply chain through personalized data collection, reasonable scheduling of computing tasks, event-driven computing mode, and secure data interaction, improves the flexibility and adaptability of the system, and enables enterprises to collaborate efficiently at different time nodes to ensure the overall efficiency and security of the supply chain.

[0035] Next, refer to Figure 2 , describing the data collection and storage strategies of the present invention.

[0036] S201: Develop personalized data collection plans for different enterprises. The key to this step is to tailor the data collection plan according to the actual operation conditions and equipment operation time of each enterprise. Specifically, if the main production activities of an enterprise occur at night, the data collection system will automatically collect relevant data during the night equipment operation to ensure the accuracy of the collection timing and the validity of the data. In one embodiment, a manufacturing enterprise usually produces from 6 pm to 6 am, and the data collection system is set to collect production data once an hour during this period to ensure the timeliness and accuracy of the data.

[0037] S202: Perform local encryption on the collected data. This step converts the original data into ciphertext through advanced encryption algorithms to enhance data security. For example, the Advanced Encryption Standard (AES-256) is used to encrypt the data to ensure that even if the data is intercepted during transmission, it cannot be decrypted. Specifically, after completing data collection, the data acquisition device immediately transmits the data to a dedicated encryption module, which uses the AES-256 algorithm to encrypt the data and generate ciphertext. This process can be completed independently on the device or on the local server to ensure that the data is protected from the source.

[0038] S203: Use distributed storage technology to store the encrypted data in distributed storage nodes locally or in the cloud. The core of this step is to store the encrypted data in multiple physical locations to improve the reliability and accessibility of the data. For example, in a specific implementation scheme, the encrypted data is divided into multiple parts and stored on server nodes located in different geographical locations. This not only reduces the risk of data loss due to a single node failure, but also allows different authorized users to quickly access the required data from the nearest node. For example, assuming that there are three nodes in a distributed storage network located in Shanghai, Beijing and Shenzhen, the encrypted data will be evenly distributed to these three nodes. When an enterprise requests access to the data center in Beijing, the data can be read directly from the node in Beijing, significantly improving access speed and efficiency.

[0039] Through the above steps, the present invention ensures the security, reliability and efficiency of data collection and storage, and is particularly suitable for asynchronous collaborative privacy computing scenarios in industrial Internet supply chains based on multi-party secure computing.

[0040] Next, the asynchronous computing task scheduling algorithm of the present invention is described.

[0041] The algorithm is used to implement asynchronous collaborative privacy computing of multi-party secure computing in the industrial Internet supply chain to ensure the effective use of computing resources and timely processing of tasks. Assume that there are n participating enterprises, each enterprise i has specific available computing resources Ri and data collection time rules Ti. The task set T = {T1, T2, ..., Tn}, and the resource set R = {R1, R2, ..., Rn}.

[0042] During the initialization process, an empty task queue Q is first established. For each enterprise i, the next possible data collection completion time ti_next is predicted based on its data collection time rule Ti. This process is to prepare task scheduling in advance to ensure that the system can allocate computing tasks at the most appropriate time. The data collection time rule Ti can be a time period customized by the enterprise, such as hourly, daily or weekly. The parameter ti_next is obtained by adding the current time to the data collection period Ti.

[0043] In the task allocation step, the current time t_current of each enterprise is checked regularly. If t_current>=ti_next, the computing task of the enterprise is added to the task queue Q. Traverse the task queue Q, and for each task Tj∈Q, find the enterprise k with sufficient available computing resources, so that Rk>=required resources (Tj). Assign task Tj to enterprise k, and update the available computing resources Rk=Rk-required resources (Tj) of enterprise k. At the same time, update the next data collection completion time ti_next of enterprise i=current time (t_current)+data collection cycle (Ti).

[0044] For example, in one embodiment, assume that there are three companies A, B, and C involved in computing tasks in the supply chain. Company A collects data once an hour, Company B collects data once every two hours, and Company C collects data once a day. At 9:00 a.m. on a certain day, when the system is initialized, Company A predicts that the next data collection completion time will be 10:00, Company B will be 11:00, and Company C will be 9:00 the next day. At 10:00 a.m. on the same day, the system detects that Company A has completed data collection and adds its computing task to the task queue Q. Assuming that the computing task of Company A requires 2 computing units and the available computing resources of Company B are 3 computing units, the system assigns the task of Company A to Company B and updates the available computing resources of Company B to 1 unit. At the same time, the next data collection completion time of Company A is updated to 11:00.

[0045] Through this mechanism, the system can dynamically allocate tasks according to the data collection time pattern of the enterprise and the currently available computing resources, ensuring efficient and fair task processing. The required resources Tj are usually determined by the complexity of the task itself and the required computing amount, while Rk represents the computing resources currently available to enterprise k. The setting of parameters ensures that tasks can be allocated to the enterprise that is most suitable for executing it according to actual conditions, thereby improving the performance and reliability of the overall system.

[0046] Next, the event-driven computing model of the present invention is described. In the asynchronous collaborative privacy computing method of the industrial Internet supply chain based on multi-party secure computing, the event-driven computing model includes multiple key steps, each of which is described in detail as follows:

[0047] First, the system monitors specific event sources, which may be situations such as data collection completion or calculation conditions being met. For example, in the industrial Internet supply chain, a manufacturing company may complete a production cycle at night, which will generate a series of data related to the production cycle. This data will be collected and uploaded to the specified location of the system. When this data is successfully collected, the system will detect this event.

[0048] Secondly, once the above events are monitored, the system will automatically trigger the corresponding computing tasks. The key to this step is to ensure that the computing tasks can accurately respond to the occurrence of events. For example, if the data collection of a manufacturing enterprise is completed, the system will automatically trigger the pre-processing and preliminary analysis tasks of the enterprise data. This mechanism ensures that the data can quickly enter the next processing stage after the collection is completed, improving the response speed and efficiency of the overall system.

[0049] Furthermore, the execution of computing tasks will be carried out according to predefined rules and processes. For example, the data preprocessing stage may include operations such as removing invalid data and cleaning noise data to ensure data quality. Preliminary analysis tasks may involve steps such as data statistics and pattern recognition to provide a basis for subsequent more complex analysis and decision-making. In this process, all computing tasks will follow the multi-party secure computing protocol to ensure data privacy and security.

[0050] In a specific embodiment, assume that there are three companies A, B and C that form a supply chain, and these companies share and collaborate on data through the Industrial Internet. At night, company A completed a production cycle, and the system detected that its data collection was completed. Subsequently, the system automatically triggered the data preprocessing and preliminary analysis tasks for company A. In this process, the preprocessing task will remove outliers and missing values ​​in the production data, and the preliminary analysis task will calculate key indicators in the production process, such as production line efficiency, defective product rate, etc. All data are processed using multi-party secure computing methods to ensure that the other two companies B and C cannot directly access or leak the data.

[0051] Next, the secure data interaction and collaborative processing algorithm of the present invention is described.

[0052] The first step of the algorithm is to establish a secure data interaction protocol based on multi-party secure computing. This means that a protocol needs to be developed to ensure that the operations performed by various enterprises at different time points can integrate their respective encrypted data. By developing standard encrypted data formats and interfaces, these data can flow smoothly between different enterprises and be accurately identified and processed. For example, in one embodiment, assume that an automobile manufacturing company needs to share production data with three different suppliers. Through a unified standard encrypted data format and interface, the four companies can send and receive each other's production data, thereby achieving effective data exchange.

[0053] The second step is to assume that there are m companies whose encrypted data need to be processed collaboratively. The data set of these companies is recorded as D = {D1, D2, ..., Dm}. Here, m represents the number of companies participating in the collaborative computing, which can be any integer greater than or equal to 2. Next, it is necessary to establish a data status list L. At the time of initialization, the data status of each company is not ready. This process is to ensure that all companies are ready to participate in the next step of joint computing to prevent some companies from being unready and causing calculation errors. For example, in the automobile manufacturing scenario, if the above four companies update their respective data status to ready, it means that they have completed the necessary preparations.

[0054] Once the encrypted data Di of a certain enterprise i is ready, it is necessary to update its status from unready to ready. This is a dynamic process, which means that each enterprise can decide when to change its status to ready based on its own readiness. In this process, there is a regular check mechanism to monitor the entire data status list L. For example, it can be automatically checked every half an hour until it is confirmed that the data status of all enterprises has been changed to ready.

[0055] When the data status of all enterprises is updated to ready, collaborative computing will be started. At this time, multi-party secure computing technology will be used to jointly calculate all encrypted data D. Common joint calculations include calculating weighted averages, sums and other statistical indicators. For example, the four companies in the automobile manufacturing industry chain can jointly calculate the total output of all parts in the past month, or calculate the average unit production cost without disclosing their specific data. After the calculation is completed, the final result will be decrypted and returned to each participating company.

[0056] In order to ensure the correctness and security of the calculation, the multi-party secure computing method used here is a technology that can protect data privacy while having computing functions. For example, when calculating the weighted average, each company provides its own output and unit production cost data as input, but all inputs are specially encrypted. By designing a suitable calculation algorithm, a commonly recognized statistical data can be calculated in the end, and there is no actual data leakage in the process. The specific algorithm formula may be adjusted according to different application scenarios, but the core principle is to maintain the privacy of the input data while outputting credible results.

[0057] In summary, the algorithm achieves efficient and privacy-safe data collaborative computing through multi-level security assurance and dynamic data status management, which is suitable for complex supply chain scenarios in the industrial Internet.

[0058] The asynchronous collaborative privacy computing method for the industrial Internet supply chain based on multi-party secure computing of the present invention includes the following key steps, which aim to solve the problem that in the industrial supply chain, there may be differences in computing resources and data availability among different enterprises, and how to allow participants to perform calculations and data interaction at different time nodes to improve the flexibility and adaptability of the system.

[0059] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0060] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0061] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0062] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0063] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0064] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium, and when the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0065] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0066] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An asynchronous collaborative privacy computing method for industrial Internet supply chain based on multi-party secure computing, characterized in that: include: Design data collection and storage strategies to provide basic data support for the entire asynchronous collaborative privacy computing; Determine the asynchronous computing task scheduling algorithm and reasonably allocate computing tasks based on the enterprise's available computing resources and data collection time rules; Design an event-driven computing model to promptly respond to the completion of enterprise data collection or the satisfaction of computing conditions, and trigger corresponding computing tasks; Design secure data interaction and collaborative processing algorithms to effectively integrate and collaboratively calculate encrypted data from different companies, providing accurate basis for overall supply chain decision-making.

2. The method for asynchronous collaborative privacy computing in industrial Internet supply chain based on multi-party secure computing according to claim 1 is characterized in that: The data collection and storage strategies include: Develop personalized data collection plans for different enterprises and collect data based on the operating time of the enterprise's production equipment; The collected data is encrypted locally; Using distributed storage technology, encrypted data is stored in distributed storage nodes locally or in the cloud to ensure data security and accessibility.

3. The method for asynchronous collaborative privacy computing in industrial Internet supply chain based on multi-party secure computing according to claim 1 is characterized in that: Asynchronous computing task scheduling algorithms include: Assume that there are n enterprises participating in the asynchronous computing of the industrial Internet supply chain. Each enterprise i has its specific available computing resources Ri and data collection time rule Ti. Define the task set T = {T1, T2, ..., Tn} and the resource set R = {R1, R2, ..., Rn}. Initialization: Establish a task queue Q, which is initially empty. For each enterprise i, predict the next possible data collection completion time ti_next based on its data collection time rule Ti; Task allocation step: regularly check the current time t_current of each enterprise. For enterprise i, if t_current>=ti_next, add the computing task of the enterprise to the task queue Q, traverse the task queue Q, and for each task Tj∈Q, find the enterprise k with sufficient available computing resources, so that Rk>=required resources (Tj), assign task Tj to enterprise k, and update the available computing resources of enterprise k Rk=Rk-required resources (Tj), and update the next data collection completion time of enterprise i ti_next=current time (t_current)+data collection cycle (Ti).

4. The method for asynchronous collaborative privacy computing in industrial Internet supply chain based on multi-party secure computing according to claim 1 is characterized in that: Event-driven computing patterns include: Using an event-driven computing model, when a company's data collection is completed or the computing conditions are met, the corresponding computing task is triggered.

5. The method for asynchronous collaborative privacy computing in industrial Internet supply chain based on multi-party secure computing according to claim 1 is characterized in that: Secure data interaction and collaborative processing algorithms include: Establish a secure data interaction protocol based on multi-party secure computing to ensure that encrypted data between enterprises operating at different time points can be effectively integrated; Assume that there are m companies whose encrypted data need to be collaboratively processed, and the data set is D = {D1, D2, ..., Dm}. A data status list L is established, and the initial status is that the data status of each company is "not ready". When the encrypted data Di of enterprise i is ready, its status is updated to "ready". The data status list L is checked regularly. When the data status of all companies is "ready", collaborative computing is started, and multi-party secure computing technology is used to jointly compute the encrypted data. After the calculation is completed, the result is decrypted and returned to each company.

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